To further increase the receptive fields while maintaining the fine-grained visual details, we propose to build a pyramid of correlation volumes in order to achieve feature matching at different spatial scales.
Particularly, rather than subsampling all the frames to the same scale as in typical feature pyramid modules [1]}, we keep the reference frame feature maps while subsampling only those of neighboring frames. By maintaining the spatial scale of the reference frame, we keep high resolution information, allowing the model to learn correspondence taking into account the fine-grained visual details.
